Image filtering (gaussian_filter) in SciPy - Mini Project: Build & Apply

Image filtering with gaussian_filter

📖 Scenario: You have a small grayscale image represented as a 2D list of pixel brightness values. You want to smooth the image to reduce noise and make it look softer, like when you blur a photo slightly.

🎯 Goal: Apply a Gaussian filter to the image data using scipy.ndimage.gaussian_filter to create a smooth version of the image.

📋 What You'll Learn

Create a 2D list called image with exact pixel values

Create a variable called sigma to set the blur strength

Use scipy.ndimage.gaussian_filter with image and sigma

Print the filtered image array

💡 Why This Matters

🌍 Real World

Image smoothing is used in photography and computer vision to reduce noise and improve image quality.

💼 Career

Understanding image filtering is important for roles in data science, machine learning, and computer vision engineering.

1

Create the image data

Create a 2D list called image with these exact pixel values: [[10, 20, 30], [20, 30, 40], [30, 40, 50]]

SciPy

# Create the 2D list called image with the given pixel values
# Your code here

Hint

Think of image as a small grid of brightness numbers.

2

Set the blur strength

Create a variable called sigma and set it to 1 to control the amount of blur.

SciPy

image = [[10, 20, 30], [20, 30, 40], [30, 40, 50]]
# Set sigma to 1
# Your code here

Hint

The sigma value controls how much the image will be blurred.

3

Apply the Gaussian filter

Import gaussian_filter from scipy.ndimage and create a variable called filtered_image by applying gaussian_filter to image with sigma.

SciPy

image = [[10, 20, 30], [20, 30, 40], [30, 40, 50]]
sigma = 1
# Import gaussian_filter and apply it to image with sigma
# Your code here

Hint

Use gaussian_filter(image, sigma) to smooth the image.

4

Print the filtered image

Print the variable filtered_image to see the smoothed image values.

SciPy

from scipy.ndimage import gaussian_filter

image = [[10, 20, 30], [20, 30, 40], [30, 40, 50]]
sigma = 1

filtered_image = gaussian_filter(image, sigma)
# Print the filtered_image
# Your code here

Hint

The printed output shows the smoothed pixel values as a NumPy array.

Practice

1. What is the main purpose of using gaussian_filter in image processing?

easy

A. To detect edges in the image

B. To smooth the image by reducing noise

C. To convert the image to grayscale

D. To increase the image resolution

Solution

1. Step 1: Understand the function's role

   gaussian_filter applies a blur effect that smooths the image by averaging nearby pixels weighted by a Gaussian curve.

2. Step 2: Identify the effect on image quality

   This smoothing reduces noise and small details, making the image less sharp but cleaner.

3. Final Answer:

   To smooth the image by reducing noise -> Option B

4. Quick Check:

   Gaussian blur = noise reduction [OK]

Hint: Gaussian filter smooths images by blurring noise away [OK]

Common Mistakes:

• Thinking it increases resolution
• Confusing it with edge detection
• Assuming it changes color format

2. Which of the following is the correct way to import and apply a Gaussian filter with sigma=2 to a 2D numpy array named image?

easy

A. from scipy.ndimage import gaussian_filter
filtered = gaussian_filter(image, sigma=2)

B. import scipy
filtered = scipy.gaussian_filter(image, 2)

C. from scipy import gaussian_filter
filtered = gaussian_filter(image, 2)

D. import gaussian_filter from scipy.ndimage
filtered = gaussian_filter(image, sigma=2)

Solution

1. Step 1: Check the correct import statement

   The Gaussian filter is in scipy.ndimage, so import it with from scipy.ndimage import gaussian_filter.

2. Step 2: Verify function usage

   Apply it by calling gaussian_filter(image, sigma=2) to blur with sigma 2.

3. Final Answer:

   from scipy.ndimage import gaussian_filter filtered = gaussian_filter(image, sigma=2) -> Option A

4. Quick Check:

   Correct import and sigma usage = from scipy.ndimage import gaussian_filter filtered = gaussian_filter(image, sigma=2) [OK]

Hint: Import from scipy.ndimage, use sigma as keyword [OK]

Common Mistakes:

• Wrong import path for gaussian_filter
• Passing sigma as positional without keyword
• Using incorrect import syntax

3. Given the code below, what will be the output array after applying the Gaussian filter?

import numpy as np
from scipy.ndimage import gaussian_filter

image = np.array([[0, 0, 0],
                  [0, 10, 0],
                  [0, 0, 0]])
filtered = gaussian_filter(image, sigma=1)
print(np.round(filtered, 2))

medium

A. [[0.46 1.23 0.46]
[1.23 2.99 1.23]
[0.46 1.23 0.46]]

B. [[0 0 0]
[0 10 0]
[0 0 0]]

C. [[2.5 2.5 2.5]
[2.5 2.5 2.5]
[2.5 2.5 2.5]]

D. [[0.1 0.1 0.1]
[0.1 10 0.1]
[0.1 0.1 0.1]]

Solution

1. Step 1: Understand Gaussian filter effect on sparse image

   The single bright pixel (10) will blur into neighbors, spreading intensity smoothly.

2. Step 2: Calculate approximate blurred values

   Using sigma=1, the center pixel reduces from 10 to about 3, neighbors get values around 1.2 to 0.46.

3. Final Answer:

   [[0.46 1.23 0.46] [1.23 2.99 1.23] [0.46 1.23 0.46]] -> Option A

4. Quick Check:

   Blur spreads intensity smoothly = [[0.46 1.23 0.46] [1.23 2.99 1.23] [0.46 1.23 0.46]] [OK]

Hint: Gaussian blur spreads bright pixels softly to neighbors [OK]

Common Mistakes:

• Expecting no change in array
• Assuming uniform average instead of weighted blur
• Misreading sigma effect as sharpening

4. Identify the error in the following code snippet that applies a Gaussian filter:

import numpy as np
from scipy.ndimage import gaussian_filter

image = np.ones((5,5))
filtered = gaussian_filter(image, sigma='2')
print(filtered)

medium

A. gaussian_filter cannot be applied to numpy arrays

B. The array shape is invalid for gaussian_filter

C. Missing import for numpy

D. sigma should be a number, not a string

Solution

1. Step 1: Check parameter types

   The sigma parameter must be a numeric value (int or float), not a string.

2. Step 2: Identify the cause of error

   Passing '2' as a string causes a type error when the filter tries to compute the blur.

3. Final Answer:

   sigma should be a number, not a string -> Option D

4. Quick Check:

   Numeric sigma required, string causes error [OK]

Hint: Use numeric sigma, not string, to avoid errors [OK]

Common Mistakes:

• Passing sigma as string
• Assuming gaussian_filter needs special array types
• Ignoring import errors

5. You have a noisy grayscale image stored as a 2D numpy array. You want to smooth the image but keep edges relatively sharp. Which approach using gaussian_filter and its parameters is best?

hard

A. Apply gaussian_filter multiple times with sigma=3

B. Use a large sigma value like 5 to blur the image heavily

C. Use a small sigma value like 0.5 to reduce noise but preserve edges

D. Do not use gaussian_filter; use median filter instead

Solution

1. Step 1: Understand sigma effect on smoothing

   Small sigma values cause light blur, preserving edges better than large sigma which blurs heavily.

2. Step 2: Choose best sigma for noise reduction and edge preservation

   Using sigma=0.5 smooths noise but keeps edges sharper than larger sigma values or repeated blurring.

3. Final Answer:

   Use a small sigma value like 0.5 to reduce noise but preserve edges -> Option C

4. Quick Check:

   Small sigma = smooth noise + keep edges [OK]

Hint: Small sigma blurs less, preserving edges better [OK]

Common Mistakes:

• Using large sigma blurs edges too much
• Applying filter multiple times causes over-blur
• Confusing gaussian_filter with median filter